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J-Wave Detection and Classification Method Based on Probabilistic Neural Network

A technology of probabilistic neural network and classification method, applied in the field of J wave detection and classification, can solve the problem of no effective J wave detection and classification.

Active Publication Date: 2018-02-27
TAIYUAN UNIV OF TECH
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AI Technical Summary

Problems solved by technology

[0004] In order to solve the problem that there is no method for effectively detecting and classifying J waves, the present invention provides a method for detecting and classifying J waves based on a probabilistic neural network

Method used

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  • J-Wave Detection and Classification Method Based on Probabilistic Neural Network

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Embodiment Construction

[0015] The J-wave detection and classification method based on the probability neural network comprises the following steps:

[0016] Obtain the required ECG signals through the electrocardiograph, including three types of ECG signals including normal ECG signal NJ, ECG signal with benign J wave ECG signal BJ, and ECG signal with high-risk J wave ECG signal MJ;

[0017] The ST segment of each ECG signal is extracted by wavelet packet transform, and the ST segment power and wavelet coefficient are obtained as two feature vectors;

[0018] The Hilbert-Huang transform is used to extract the features of the extracted ST segment, that is, the empirical mode decomposition is carried out first, and the ST segment signal is decomposed into a series of intrinsic mode functions IMF, and then each intrinsic mode function IMF is performed. Albert-Huang transform to get its instantaneous frequency and magnitude as two eigenvectors;

[0019] Three groups of ECG signals including normal ECG...

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Abstract

The invention relates to a J-wave detection and classification method, in particular to a J-wave detection and classification method based on a probability neural network. The present invention firstly obtains the required electrocardiographic signal through the electrocardiograph, utilizes wavelet packet transformation to extract the ST segment of the electrocardiographic signal, and obtains the power of the ST segment and wavelet coefficients, and uses the Hilbert-Huang transform for the extracted ST segment. Feature extraction, get its instantaneous frequency and amplitude, use the probabilistic neural network as the J-wave detection classifier, input the feature vector into PNN, and train the PNN; then take the test sample, perform the same preprocessing on it, and extract 4 features Vector, input into PNN to get its classification result. The invention proposes a simple and effective J wave detection and classification method, which provides a basis for doctors to identify high-risk patients with clinically abnormal J waves.

Description

technical field [0001] The invention relates to a J-wave detection and classification method, in particular to a J-wave detection and classification method based on a probability neural network. Background technique [0002] The J wave refers to the dome-shaped or hump-shaped potential change between the QRS wave and the ST segment on the electrocardiogram (ECG). Under normal circumstances, the J wave is partially or completely hidden in the QRS complex. Obvious abnormal J waves are often associated with hypothermia, hypercalcemia, and nervous system damage. Clinical syndromes or disorders caused by J waves are called J wave syndromes and include: early repolarization syndrome, Brugada syndrome, sudden cardiac death with prominent J waves in inferior leads, and ST-segment depression in inferior leads Elevated primary cardiac arrest, etc. [0003] Electrocardiographic J wave and J wave syndrome are high-risk early warning indicators of sudden cardiac death. Screening of s...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A61B5/0452
Inventor 李灯熬王欣赵菊敏
Owner TAIYUAN UNIV OF TECH
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